• Journal of Digital Contents Society
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• v.12 no.4
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• pp.429-436
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• 2011

Various documents, images, videos and other materials on the web has been increasing rapidly. Efficient search of those things has become an important topic. From keyword-based search, internet search has been transformed to semantic search which finds the implications and the relations between data elements. Many annotation processing systems manipulating the metadata for semantic search have been proposed. However, annotation data generated by different methods and forms are difficult to process integrated search between those systems. In this study, in order to resolve this problem, we categorized levels of many annotation documents, and we proposed the method to measure the similarity between the annotation documents. Similarity measure between annotation documents can be used for searching similar or related documents, images, and videos regardless of the forms of the source data.

• Journal of Korean Library and Information Science Society
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• v.48 no.4
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• pp.351-374
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• 2017

In this study, we propose a model for constructing a producer-centered self-publishing repository for the personal digital records and three operational process for practical application of the operational model. 1) As essential requirements for constructing a DSpace-based self-publishing repository, we propose producer-centered service provision, management subject, operation and management plan, how to activate the repository of digital personal records producers, copyright issues, development and dissemination process. 2) The repository model constructs a producer-centered circular structure considering these requirements. 3) Through a repository model with multiple agencies, it provides various services such as content distribution, keyword search, usage statistics, recommendation system, and open access to portal users and electronic publishers as well as individual users.

• The Journal of the Korea Contents Association
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• v.12 no.12
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• pp.35-45
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• 2012

Researchers and scientists spend huge amount of time in analyzing the previous studies and their results. In order to timely take the advantageous position, they usually analyze various resources such as paper, patents, and Web documents on recent research issues to preoccupy newly emerging technologies. However, it is difficult to select invest-worthy research fields out of huge corpus by using the traditional information search based on keywords and bibliographic information. In this paper, we propose a method for efficient creation, storage, and utilization of semantically relevant information among technologies, products and research agents extracted from 'big data' by using text mining. In order to implement the proposed method, we designed an ontology that creates technological knowledge for semantic web environment based on the relationships extracted by text mining techniques. The ontology was utilized for InSciTe Adaptive, a R&D trends analysis and forecast service which supports the search for the relevant technological knowledge.

• Journal of the Korean Society for Library and Information Science
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• v.35 no.4
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• pp.97-122
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• 2001

This empirical study investigated the effect of choice of record field(s) upon which to search on retrieval performance for a large operational bibliographic database. The query terms used in the study were identified algorithmically from each target set in four different ways: (1) controlled terms derived from index term frequency weights, (2) uncontrolled terms derived from index term frequency weights. (3) controlled terms derived from inverse document frequency weights, and (4) uncontrolled terms based on universe document frequency weights. Su potable choices of record field were recognised. Using INSPEC terminology, these were the fields: (1) Abstract. (2) 'Anywhere'(i.e., ail fields). (3) Descriptors. (4) Identifiers, (5) 'Subject'(i.e., 'Descriptors' plus Identifiers'). and (6) Title. The study was undertaken in an operational web-based IR environment using the INSPEC bibliographic database. The retrieval performances were evaluated using D measure (bivariate in Recall and Precision). The main findings were that: (1) there exist significant differences in search performance arising from choice of field, using 'mean performance measure' as the criterion statistic; (2) the rankings of field-choices for each of these performance measures is sensitive to the choice of query : and (3) the optimal choice of field for the D-measure is Title.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.5
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• pp.207-214
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• 2020

Nowadays, the use of smart phones and various electronic devices is increasing, the Internet and SNS are activated, and we live in the flood of information. The amount of information has grown exponentially, making it difficult to look at a lot of information, and more and more people want to see only key keywords in a document, and the importance of research to extract topics that are the core of information is increasing. In addition, it is also an important issue to extract the topic and compare it with the past to infer the current trend. Topic modeling techniques can be used to extract topics from a large volume of documents, and these extracted topics can be used in various fields such as trend prediction and data analysis. In this paper, we inquire the topic of the three-year papers of 2016, 2017, and 2018 in the field of computing using the LDA algorithm, one of Probabilistic Topic Model Techniques, in order to analyze the rapidly changing trends and keep pace with the times. Then we analyze trends and flows of research.

• Journal of Broadcast Engineering
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• v.24 no.1
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• pp.77-86
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• 2019

This paper proposes using machine-learning technology to analyze and classify historical collected documents based on them. Data is collected based on keywords associated with a specific domain and the non-conceptuals such as special characters are removed. Then, tag each word of the document collected using a Korean-language morpheme analyzer with its nouns, verbs, and sentences. Embedded documents using Doc2Vec model that converts documents into vectors. Measure the similarity between documents through the embedded model and learn the document classifier using the machine running algorithm. The highest performance support vector machine measured 0.83 of F1-score as a result of comparing the classification model learned.

• Korean Journal of Construction Engineering and Management
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• v.22 no.1
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• pp.13-26
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• 2021

To promote the off-site construction (OSC) in Korea, technical innovation applied to each phase, such as design, engineering, factory production, and site assembly, is important and equally necessary is the development of management and operation methods that are different from existing construction production methods. However, the current OSC-related studies in Korea are conducted from the technical development viewpoints, such as construction methods. Additionally, few studies have been conducted to derive a management measure for successful OSC projects. Therefore, studies to derive a management measure based on a clear understanding of the core success factors of OSC projects are required. This study aims to analyze several studies related to the success factors of OSC projects conducted overseas and to show its core implications for the successful management of OSC projects in Korea. We expect this study to improve the viability of OSC projects, which will be expanded in Korea in the future.

• Communications of Mathematical Education
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• v.34 no.4
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• pp.545-561
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• 2020

The purpose of this study is to investigate the applications and possibilities of major programs that provide services using artificial intelligence in mathematics education. For this study, related papers, reports, and materials were collected and analyzed, focusing on materials mostly published within the last five years. The researcher searched the keywords of "artificial intelligence", "artificial intelligence", "AI" and "mathematics education" independently or in combination. As a result of the study, artificial intelligence for mathematics education was mostly supporting learners' personalized mathematics learning, defining it as an auxiliary role to support human mathematics teachers, and upgrading the technology of not only cognitive aspects but also affective aspects. As suggestions, the researcher argued that followings are necessary: Research for the establishment of an elaborate artificial intelligence mathematical system, discovery of artificial intelligence technology for appropriate use to support mathematics education, development of high quality of mathematics contents for artificial intelligence, and the establishment and operation of a cloud-based comprehensive system for mathematics education. The researcher proposed that continuous research to effectively help students study mathematics using artificial intelligence including students' emotional or empathetic abilities, and collaborative learning, which is only possible in offline environments. Also, the researcher suggested that more sophisticated materials should be developed for designing mathematics teaching and learning by using artificial intelligence.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.205-210
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• 2022

Drones, which have rapidly grown along with the 4th industrial revolution, spread over industries and also widely used for military purposes. In recent wars in Europe, drones are being evaluated as a game changer on the battlefield, and their importance for military use is being highlighted. The Republic of Korea Army also planned drone-bot systems including various drones suitable for echelons and missions of the military as future defense forces. The keyword of these drone-bot systems is autonomy by artificial intelligence. In addition, common use of operating platforms is required for the rapid development of various types of drones. In this paper, we propose software framework that applies diverse artificial intelligence technologies such as multi-agent system, cognitive architecture and knowledge-based context reasoning for mission autonomy and common use of military drones.

• Journal of Technology Innovation
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• v.18 no.2
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• pp.59-89
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• 2010

One of the key words that represent the culture and social phenomena in this 21st century is 'Digital Convergence'. Presently our society is going through the second evolving phase of the convergence, integration between industries. Integration between industries refers to the phenomenon of integrating various industrial areas such as broadcasting, communication, finance, automobile, and medical treatment as the range of IT utilization becomes extended. The telecommunication-broadcasting bundling is a typical example of integration between industries. This study analyzed the effect of the service features of the bundling on the customer's acceptance intention by applying the innovative technology product acceptance model, TAM, in the process of accepting the telecommunication-broadcasting bundling. This study suggests three independent variables, the 'low cost(discount)', 'service integrity', and 'selectability', which affect perceived usefulness, perceived ease of use, and perceived risk, and figures out the actual influence as follows. In conclusion, these results suggest that in order to accept and spread the telecommunication-broadcasting bundling, it is necessary to establish (1) the cost discounting strategy realize (price strategy), (2) the fee noticing system, payment system, call-service system, and systemic integration including installation and A/S system integration, and develop (service strategy), (3) the bundling or related options that can provide users with selectability (product strategy).